高分六号（GF-6）卫星于2018年成功发射，2019-03正式投入使用。由于GF-6宽幅相机的WFV（Wide Field of View）影像较GF-1的同类影像新增2个红边波段，将会提高对农业、林业、草原等资源监测能力。为了分析GF-6的WFV影像在人工林分类方面的能力，促进高分数据在林业领域的应用，本文选取广西高峰林场为研究区，以最新的GF-6 WFV影像为数据源，结合地面实测类型数据，进行广西南宁高峰林场的桉树，杉木等人工林类型提取。主要运用随机森林（random forests）的分层分类法：首先计算6种植被指数，并利用随机森林法进行植被指数的特征优选，然后确定4种波段组合数据集的分类方案，4种数据集分别为（1）无红边的前4个波段，（2）有红边的8个波段，（3）8个波段加上未优化的植被指数特征组合，（4）8个波段加上优化的植被指数特征组合。再进行WFV影像4种数据集的随机森林分类，随机森林采用分类回归树（CART）算法来生成分类树，结合了bagging和随机选择特征变量的优点，是一种有效的分类方法。最后比较4个方案的分类结果并进行精度验证。结果表明：方案2比方案1精度提高了4.99%，Kappa系数提高了0.058。说明包含红边的8波段数据比4个波段数据精度有大幅提升。方案4的8波段加上优化植被指数特征组合的分类精度最高，达到了85.38%，比方案2包含红边波段组和方案1无红边波段组的精度分别提高了3.98%，8.97%，Kappa系数分别提高了0.046，0.104。说明WFV影像加入红边波段比无红边波段精度明显增高。由结果可知，红边指数的引入，增强了植被信息，能够较准确地反映人工林类型特征差异，明显提升了人工林的分类精度。本研究方法可以有效改善广西人工林类型信息提取效果，为GF-6影像质量的评价及其在林业应用潜力提供科学参考依据。

The high-spatial-resolution GF-6 satellite has been successfully launched for less than one year, and the application of its imagery has just started. GF-6 is a satellite of a high-resolution earth observation system, which is a major scientific and technological project in China. The Wide Field-of-View (WFV) images of the GF-6 wide-format camera add two red-edged bands in comparison with similar images of the GF-1; thus, the monitoring capacity of agriculture, forestry, and grassland is improved. To analyze the ability of GF-6 WFV imagery in plantation classification and promote the further application of GF-6 data in forestry field, this study provides a hierarchical classification method to extract plantation types, such as eucalyptus and fir, by using the latest WFV images of GF-6 in the Nanning forest farm in Guangxi Province. Furthermore, the classification process was combined with ground measured data.The random forest classification method was adopted, and the steps are as follows: First, the six vegetation indices were calculated and optimized using the random forest feature selection method, and then the classification scheme of the four datasets was determined. The schemes are as follows: (1) first four bands of WFV image without red edge, (2) eight bands with red edge, (3) eight bands plus unoptimized vegetation index features, and (4) eight bands plus optimized vegetation index features. Then, the random forest classifier was utilized in four datasets. Random forest is an effective classification method that uses the classification regression tree algorithm to generate classification trees and combines the advantages of bagging and the random selection of feature variables. Lastly, the classification results of four schemes were compared; in addition, accuracy assessment was performed in accordance with field survey and forestry inventory data.Results showed that Scheme 2 has an accuracy of 4.99% higher than that of Scheme 1, and the kappa coefficient increased by 0.058. These results indicate that the accuracy of eight-band data with red edges is significantly improved in comparison with four-band data.The classification accuracy of the eight bands plus optimized vegetation index features was the highest among the schemes, reaching 85.38%. Compared with the bands with and without red edge, the accuracy improved by 3.98% and 8.97%, respectively; moreover, the kappa coefficient increased by 0.046 and 0.104.WFV images are added with the red-edge index, thereby enhancing vegetation information. This dataset can accurately reflect the differences of plantation type characteristics and significantly improve the classification accuracy of the plantation. Therefore, this study can effectively improve the information extraction effect of plantation types in Guangxi Province and provide a scientific reference for the evaluation of GF-6 image quality and its forestry application potential.